An energy saving small cell sleeping mechanism with cell range expansion in heterogeneous networks

In recent years, the explosion of wireless data traffic has resulted in a trend of large scale dense deployment of small cells, with which the rising cost of energy has attracted a lot of research interest. In this paper, we present a novel sleeping mechanism for small cells to decrease the energy consumption of heterogeneous networks. Specifically, in the cell-edge area of a macrocell, the small cells will be put into sleep where possible and their service areas will be covered by the range-expanded small cells nearby and the macrocell; in areas close to the macrocell, the user equipments associated with a sleeping small cell will be handed over to the macrocell. Furthermore, we use enhanced inter-cell interference coordination techniques to support the range expanded small cells to avoid QoS degradation. Using a stochastic geometry-based network model, we provide the numerical analysis of the proposed approach, and the results indicate that the proposed sleeping mechanism can significantly reduce the power consumption of the network compared with the existing sleeping methods while guaranteeing the QoS requirement

Motion Sensor-based Small Cell Sleep Scheduling for 5G Networks

No abstract available

Q-learning Assisted Energy-Aware Traffic Offloading and Cell Switching in Heterogeneous Networks

Cell switching has been identified as a major approach to significantly reduce the energy consumption of Heterogeneous Networks (HetNets). The main idea behind cell switching is to turn off idle or lightly loaded Base Stations (BSs) and to offload their traffic to neighbouring active cell(s). However, the impact of the offloaded traffic on the power consumption of the neighbouring cell(s) has not been studied sufficiently in the literature, thereby leading to the development of sub-optimal cell switching mechanisms. In this work, we first considered a Control/Data Separated Architecture (CDSA) with a macro cell serving as the Control Base Station (CBS) and multiple small cells as Data Base Stations (DBS). Then, a Q-learning assisted cell switching algorithm is developed in order to determine the small cells to switch off by considering the increase in power consumption of the macro cell due to offloaded traffic from the sleeping cells. The capacity of the macro cell is also taken into consideration to ensure that the Quality of Service (QoS) requirements of users are maintained. Simulation results show that the proposed cell switching algorithm can achieve up to 50% reduction in the total energy consumption of the considered HetNet scenario